Introductory description

This module prepares students to lead Artificial Intelligence (AI)-driven organisations as informed architects of data-driven decision-making by introducing the modern data landscape, including the infrastructure underpinning large-scale data management, and then moving decisively to how artificial intelligence and advanced analytics methods transform raw data into actionable organisational intelligence. Students will develop fluency across the full analytics pipeline, including emerging generative AI capabilities, anchored in the management and engineering contexts in which these methods are applied.

Where this module is delivered at an overseas centre, the module will be delivered over 1 week.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Data in the Modern Enterprises

• The Data Landscape
• Core Data Concepts
• Big Data Management: Architecture and Technologies
• Modern Data Stack for Industry

Foundations of AI-Driven Analytics

• Types of Analytics: Descriptive, Diagnostic, Predictive, and Prescriptive
• Introduction to Artificial Intelligence (AI) and Machine Learning (ML)
• Applied Analytics in an Industry Context
• Introduction to Generative AI and Large Language Models (LLMs)
• Responsible AI in Analytics

Data Storytelling, Visualisation and Decision Communication

• Principles of Visual Communication
• Data Visualisation and Storytelling

AI-Driven Analytics in Engineering and Operations: Applied Capstone

• Capstone Consultancy Project

Learning outcomes

By the end of the module, students should be able to:

• Critically evaluate the capabilities, limitations, and strategic implications of modern data architectures and AI-driven analytical approaches relative to traditional methods, within engineering and technology-led business contexts (M1).
• Critically analyse complex, real-world engineering business scenarios and propose appropriate AI-driven analytical solutions with evidence-based justifications (M4).
• Critically evaluate visual communication principles and data visualisation design to construct analytical narratives that effectively translate complex findings for diverse stakeholder audiences (M17).
• Collaboratively analyse engineering business requirements and practically implement end-to-end AI-driven analytics pipeline to deliver actionable, evidence-based recommendations in realistic industry settings (M2, M3, M5, M7).
• Produce a critical reflection on the learning throughout the module, evaluating the development of analytical competence and the implications for continued professional practice in modern engineering business management (M16).

Indicative reading list

Reading lists can be found in Talis

Specific reading list for the module

Interdisciplinary

A mixture of technology/computing topics and business topics

International

Topics are of high demand internationally

Subject specific skills

• Compare different data architectures.
• Use Big Data tools and frameworks to assess their value and impact in organisations.
• Design and run data analysis workflows.
• Apply and evaluate machine learning models for real-world datasets.
• Interpret the results of machine learning models.
• Use AI methods to support data analysis and decision-making.
• Apply data visualisation techniques and tools.
• Present data analysis results to different audiences.
• Recognise key issues in data governance, ethics, and regulation.

Transferable skills

• Apply statistical and quantitative methods to analyse data and draw conclusions.
• Use programming tools to build, test, and present data solutions.
• Critically assess methods, model results, and business problems using evidence.
• Work effectively as part of a team.
• Communicate complex ideas clearly to non-technical audiences.
• Use structured approaches to solve problems and support decision-making in uncertain situations.